Selecting Items of Relevance in Social Network Feeds
The success of online social networking systems has revolutionised online sharing and communication, however it has also contributed significantly to the infamous information overload problem. Social Networking systems aggregate network activities into chronologically ordered lists, Network Feeds, as a way of summarising network activity for its users. Unfortunately, these feeds do not take into account the interests of the user viewing them or the relevance of each feed item to the viewer. Consequently individuals often miss out on important updates. This work aims to reduce the burden on users of identifying relevant feed items by exploiting observed user interactions with content and people on the network and facilitates the personalization of network feeds in a manner which promotes relevant activities. We present the results of a large scale live evaluation which shows that personalized feeds are more successful at attracting user attention than non-personalized feeds.
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